MMR-and-RMR-workflow-Clutch133

Load libraries (packages)

library("respR") ## respirometry/slope analysis
library("tidyverse") ## data manipulation

Set working directory

setwd("[PATH TO DIRECTORY]")

System1 - Dell

Importing data from firesting for resting

preexperiment_date <- "13 May 2023 11 14AM/All"
postexperiment_date <- "13 May 2023 03 52PM/All"

##--- last fish run in trial ---##
experiment_date <- "13 May 2023 12 30PM/Oxygen"
experiment_date2 <- "13 May 2023 12 30PM/All"

firesting <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19)

Cycle_1 <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

Cycle_last <-read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date2,"slopes/Cycle_21.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

System2 - Asus

Importing data from firesting for resting

preexperiment_date_asus <- "13 May 2023 11 07AM/All"
postexperiment_date_asus <- "13 May 2023 04 23PM/All"

##--- last fish run in trial ---##
experiment_date_asus <- "13 May 2023 01 13PM/Oxygen"
experiment_date2_asus <- "13 May 2023 01 13PM/All"

firesting_asus <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19)

Cycle_1_asus <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

Cycle_last_asus <-read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date2_asus,"slopes/Cycle_21.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

Chamber volumes

chamber1_dell = 0.04650
chamber2_dell = 0.04593
chamber3_dell = 0.04977
chamber4_dell = 0.04860 

chamber1_asus = 0.04565
chamber2_asus = 0.04573
chamber3_asus = 0.04551
chamber4_asus = 0.04791

Date_tested="2023-05-13"
Clutch = "100" 
Male = "CARL249" 
Female = "CARL360"
Population = "Arlington reef"
Tank =373 
salinity =36 
Date_analysed = Sys.Date() 

Replicates

1

Enter specimen data

Replicate = 1 
mass = 0.0005823 
chamber = "ch4" 
Swim = "good/good"
chamber_vol = chamber4_dell
system1 = "Dell"
Notes="check max and rest"

##--- time of trail ---## 
experiment_mmr_date <- "13 May 2023 11 50AM/Oxygen"
experiment_mmr_date2 <- "13 May 2023 11 50AM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] 1.572533e-05

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 

## [1] -0.001569484

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.07 10.44
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME) 
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  6  8  9 10 11 12 13 14 15 16 18 19 20 21 22 23 26
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.07 10.44
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.

## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve). 
## Ensure this is correct. The 'linear' adjustment has been performed regardless.

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:  14    1     344.7483 -0.02083626 0.993      NA 6346   6579 11769.91
## 2:  17    1     377.8635 -0.02076999 0.989      NA 7825   8058 13389.93
## 3:  18    1     329.4044 -0.01647411 0.987      NA 8319   8553 13930.39
## 4:  19    1     364.8815 -0.01830641 0.991      NA 8812   9045 14469.47
## 5:  20    1     366.0873 -0.01771942 0.987      NA 9306   9539 15010.30
## 6:  21    1     341.7865 -0.01555060 0.985      NA 9799  10032 15549.70
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 12025.09 99.195 93.983 -0.02083626 -0.001207550   -0.01962871 -0.01962871
## 2: 13644.61 99.551 94.084 -0.02076999 -0.001463498   -0.01930649 -0.01930649
## 3: 14186.02 99.538 95.403 -0.01647411 -0.001548974   -0.01492513 -0.01492513
## 4: 14724.33 99.673 95.180 -0.01830641 -0.001634096   -0.01667232 -0.01667232
## 5: 15264.90 99.899 95.834 -0.01771942 -0.001719535   -0.01599988 -0.01599988
## 6: 15805.01 99.750 95.726 -0.01555060 -0.001804825   -0.01374577 -0.01374577
##    oxy.unit time.unit volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.0486 0.0005823   NA 36 30 1.013253 -0.2124061
## 2:     %Air       sec 0.0486 0.0005823   NA 36 30 1.013253 -0.2089192
## 3:     %Air       sec 0.0486 0.0005823   NA 36 30 1.013253 -0.1615077
## 4:     %Air       sec 0.0486 0.0005823   NA 36 30 1.013253 -0.1804143
## 5:     %Air       sec 0.0486 0.0005823   NA 36 30 1.013253 -0.1731378
## 6:     %Air       sec 0.0486 0.0005823   NA 36 30 1.013253 -0.1487457
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -364.7709          NA  mgO2/hr/kg   -364.7709
## 2:   -358.7828          NA  mgO2/hr/kg   -358.7828
## 3:   -277.3617          NA  mgO2/hr/kg   -277.3617
## 4:   -309.8305          NA  mgO2/hr/kg   -309.8305
## 5:   -297.3344          NA  mgO2/hr/kg   -297.3344
## 6:   -255.4451          NA  mgO2/hr/kg   -255.4451
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
100	1	CARL249	CARL360	Arlington reef	373	0.0005823	ch4	Dell	0.0486	2023-05-13	2024-06-14	good/good	36	30	321.6161	0.187277	0.9894

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.08 1.74
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  4  5  7 10 11 12 13 14 15 16 18 19 23 24 26 27 28 29
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.08 1.41
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0     slope_b1       rsq density row endrow    time
##   1:  NA    1     243.4814 -0.061147538 0.9567140      NA 208    261 2541.84
##   2:  NA    2     243.4393 -0.061137204 0.9568215      NA 207    260 2540.46
##   3:  NA    3     240.4646 -0.061084948 0.9926824      NA  94    147 2409.68
##   4:  NA    4     240.4602 -0.061084638 0.9926865      NA  93    146 2408.57
##   5:  NA    5     240.3756 -0.061046829 0.9925918      NA  95    148 2410.77
##  ---                                                                        
## 205:  NA  205     106.4797 -0.007403344 0.7140865      NA 167    220 2494.47
## 206:  NA  206     105.9608 -0.007205789 0.7566911      NA 171    224 2499.19
## 207:  NA  207     105.6227 -0.007067061 0.7438188      NA 168    221 2495.56
## 208:  NA  208     105.3331 -0.006956580 0.7550250      NA 170    223 2498.04
## 209:  NA  209     105.2373 -0.006917017 0.7554722      NA 169    222 2496.65
##      endtime    oxy endoxy         rate
##   1: 2601.84 87.704 84.781 -0.061147538
##   2: 2600.46 87.693 84.755 -0.061137204
##   3: 2469.68 93.201 89.698 -0.061084948
##   4: 2468.57 93.237 89.740 -0.061084638
##   5: 2470.77 93.134 89.660 -0.061046829
##  ---                                   
## 205: 2554.47 88.446 87.495 -0.007403344
## 206: 2559.19 88.063 87.396 -0.007205789
## 207: 2555.56 88.357 87.446 -0.007067061
## 208: 2558.04 88.181 87.421 -0.006956580
## 209: 2556.65 88.295 87.431 -0.006917017
## 
## Regressions : 209 | Results : 209 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 209 adjusted rate(s):
## Rate          : -0.06114754
## Adjustment    : 1.572533e-05
## Adjusted Rate : -0.06116326 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.93 and 1...

## ----- Selection complete. 64 rate(s) removed, 145 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 144 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1      rsq density row endrow    time
## 1:  NA    1     243.4814 -0.06114754 0.956714      NA 208    261 2541.84
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 2601.84 87.704 84.781 -0.06114754 1.572533e-05   -0.06116326 -0.06116326
##    oxy.unit time.unit volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.0486 0.0005823   NA 36 30 1.013253 -0.6618594
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:    -1136.63          NA  mgO2/hr/kg    -1136.63
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
100	1	CARL249	CARL360	Arlington reef	373	0.0005823	ch4	Dell	0.0486	2023-05-13	2024-06-14	good/good	36	30	321.6161	0.187277	0.9894	1136.63	0.6618594	0.956714	815.0135	0.4745824	check max and rest

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 100 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

2

Enter specimen data

Replicate = 2 
mass = 0.0004707 
chamber = "ch3" 
Swim = "good/good"
chamber_vol = chamber3_dell
system1 = "Dell"
Notes="max and rest seems low"

##--- time of trail ---## 
experiment_mmr_date <- "13 May 2023 12 19PM/Oxygen"
experiment_mmr_date2 <- "13 May 2023 12 19PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] -0.0005855578

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 

## [1] -0.002506235

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.07 10.44
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME) 
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  6  8  9 10 11 12 13 14 15 16 18 19 20 21 22 23 26
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.07 10.44
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:   9    1     263.3727 -0.01812897 0.993      NA 3890   4119  9069.71
## 2:  12    1     299.9328 -0.01872412 0.971      NA 5360   5593 10690.04
## 3:  13    1     301.9229 -0.01801757 0.991      NA 5853   6086 11230.32
## 4:  15    1     321.8125 -0.01804544 0.995      NA 6839   7073 12310.07
## 5:  19    1     346.6633 -0.01707441 0.983      NA 8812   9045 14469.47
## 6:  20    1     375.2195 -0.01833765 0.995      NA 9306   9539 15010.30
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1:  9324.80 98.710 94.078 -0.01812897 -0.001550731   -0.01657823 -0.01657823
## 2: 10945.31 99.387 94.513 -0.01872412 -0.001860969   -0.01686315 -0.01686315
## 3: 11485.43 99.454 94.653 -0.01801757 -0.001964393   -0.01605318 -0.01605318
## 4: 12565.60 99.689 95.169 -0.01804544 -0.002171157   -0.01587428 -0.01587428
## 5: 14724.33 99.442 95.348 -0.01707441 -0.002584521   -0.01448989 -0.01448989
## 6: 15264.90 99.771 95.213 -0.01833765 -0.002688040   -0.01564961 -0.01564961
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04977 0.0004707   NA 36 30 1.013253 -0.1837151
## 2:     %Air       sec 0.04977 0.0004707   NA 36 30 1.013253 -0.1868724
## 3:     %Air       sec 0.04977 0.0004707   NA 36 30 1.013253 -0.1778965
## 4:     %Air       sec 0.04977 0.0004707   NA 36 30 1.013253 -0.1759141
## 5:     %Air       sec 0.04977 0.0004707   NA 36 30 1.013253 -0.1605727
## 6:     %Air       sec 0.04977 0.0004707   NA 36 30 1.013253 -0.1734243
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -390.3018          NA  mgO2/hr/kg   -390.3018
## 2:   -397.0097          NA  mgO2/hr/kg   -397.0097
## 3:   -377.9403          NA  mgO2/hr/kg   -377.9403
## 4:   -373.7286          NA  mgO2/hr/kg   -373.7286
## 5:   -341.1359          NA  mgO2/hr/kg   -341.1359
## 6:   -368.4392          NA  mgO2/hr/kg   -368.4392
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
100	2	CARL249	CARL360	Arlington reef	373	0.0004707	ch3	Dell	0.04977	2023-05-13	2024-06-14	good/good	36	30	381.4839	0.1795645	0.989

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.08 1.74
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row,  
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  9 10 11 12 13 15 17 18 20 21 24 25 26
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.08 1.38
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     338.8036 -0.05826840 0.9947858      NA  72    126 4145.55
##   2:  NA    2     338.5456 -0.05820761 0.9946429      NA  71    125 4144.45
##   3:  NA    3     338.3196 -0.05815184 0.9945928      NA  73    127 4146.77
##   4:  NA    4     337.6773 -0.05800075 0.9940756      NA  70    124 4143.27
##   5:  NA    5     337.4374 -0.05793982 0.9941715      NA  74    128 4147.86
##  ---                                                                       
## 207:  NA  207     177.0633 -0.01920212 0.9709916      NA  13     67 4079.19
## 208:  NA  208     177.0611 -0.01919827 0.9710164      NA  10     64 4075.83
## 209:  NA  209     176.9497 -0.01917323 0.9714974      NA  12     66 4078.09
## 210:  NA  210     176.9193 -0.01916876 0.9675966      NA  14     68 4080.56
## 211:  NA  211     176.8531 -0.01914863 0.9718680      NA  11     65 4076.92
##      endtime    oxy endoxy        rate
##   1: 4205.55 97.150 93.845 -0.05826840
##   2: 4204.45 97.149 93.890 -0.05820761
##   3: 4206.77 97.170 93.795 -0.05815184
##   4: 4203.27 97.192 93.937 -0.05800075
##   5: 4207.86 97.116 93.781 -0.05793982
##  ---                                  
## 207: 4139.19 98.884 97.442 -0.01920212
## 208: 4135.83 98.970 97.589 -0.01919827
## 209: 4138.09 98.897 97.484 -0.01917323
## 210: 4140.56 98.850 97.376 -0.01916876
## 211: 4136.92 98.934 97.540 -0.01914863
## 
## Regressions : 211 | Results : 211 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 211 adjusted rate(s):
## Rate          : -0.0582684
## Adjustment    : -0.0005855578
## Adjusted Rate : -0.05768285 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.93 and 1...

## ----- Selection complete. 0 rate(s) removed, 211 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 210 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0   slope_b1       rsq density row endrow    time
## 1:  NA    1     338.8036 -0.0582684 0.9947858      NA  72    126 4145.55
##    endtime   oxy endoxy       rate    adjustment rate.adjusted  rate.input
## 1: 4205.55 97.15 93.845 -0.0582684 -0.0005855578   -0.05768285 -0.05768285
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04977 0.0004707   NA 36 30 1.013253 -0.6392241
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -1358.029          NA  mgO2/hr/kg   -1358.029
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
                      Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
100	2	CARL249	CARL360	Arlington reef	373	0.0004707	ch3	Dell	0.04977	2023-05-13	2024-06-14	good/good	36	30	381.4839	0.1795645	0.989	1358.029	0.6392241	0.9947858	976.5448	0.4596597	max and rest seems low

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 101 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

3

Enter specimen data

Replicate = 3 
mass = 0.0005800 
chamber = "ch2" 
Swim = "good/good"
chamber_vol = chamber2_dell
system1 = "Dell"
Notes=""

##--- time of trail ---## 
experiment_mmr_date <- "13 May 2023 12 10PM/Oxygen"
experiment_mmr_date2 <- "13 May 2023 12 10PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] 0.0001214543

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 

## [1] -0.001449414

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.07 10.44
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME) 
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  6  8  9 10 11 12 13 14 15 16 18 19 20 21 22 23 26
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.07 10.44
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.

## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve). 
## Ensure this is correct. The 'linear' adjustment has been performed regardless.

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 1 rate(s) removed, 20 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 14 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:   1    1     190.6061 -0.01959003 0.965      NA   40    263  4749.85
## 2:   4    1     267.2548 -0.02632660 0.977      NA 1461   1689  6370.38
## 3:   5    1     251.7973 -0.02201686 0.984      NA 1946   2177  6909.90
## 4:  10    1     328.7728 -0.02405259 0.991      NA 4377   4608  9609.37
## 5:  14    1     421.2917 -0.02742542 0.993      NA 6346   6579 11769.91
## 6:  19    1     471.2868 -0.02574880 0.994      NA 8812   9045 14469.47
##     endtime    oxy endoxy        rate    adjustment rate.adjusted  rate.input
## 1:  5005.67 98.117 92.054 -0.01959003  8.437034e-06   -0.01959846 -0.01959846
## 2:  6625.01 98.823 92.556 -0.02632660 -2.452217e-04   -0.02608138 -0.02608138
## 3:  7165.42 99.243 93.820 -0.02201686 -3.297725e-04   -0.02168709 -0.02168709
## 4:  9864.74 97.622 91.235 -0.02405259 -7.524592e-04   -0.02330014 -0.02330014
## 5: 12025.09 98.555 91.833 -0.02742542 -1.090754e-03   -0.02633466 -0.02633466
## 6: 14724.33 98.661 92.408 -0.02574880 -1.513442e-03   -0.02423535 -0.02423535
##    oxy.unit time.unit  volume    mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04593 0.00058   NA 36 30 1.013253 -0.2004275
## 2:     %Air       sec 0.04593 0.00058   NA 36 30 1.013253 -0.2667263
## 3:     %Air       sec 0.04593 0.00058   NA 36 30 1.013253 -0.2217872
## 4:     %Air       sec 0.04593 0.00058   NA 36 30 1.013253 -0.2382834
## 5:     %Air       sec 0.04593 0.00058   NA 36 30 1.013253 -0.2693165
## 6:     %Air       sec 0.04593 0.00058   NA 36 30 1.013253 -0.2478476
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -345.5647          NA  mgO2/hr/kg   -345.5647
## 2:   -459.8729          NA  mgO2/hr/kg   -459.8729
## 3:   -382.3918          NA  mgO2/hr/kg   -382.3918
## 4:   -410.8334          NA  mgO2/hr/kg   -410.8334
## 5:   -464.3389          NA  mgO2/hr/kg   -464.3389
## 6:   -427.3234          NA  mgO2/hr/kg   -427.3234
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple")  

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
100	3	CARL249	CARL360	Arlington reef	373	0.00058	ch2	Dell	0.04593	2023-05-13	2024-06-14	good/good	36	30	428.9521	0.2487922	0.9878

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.08 1.74
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  2  3  4  6  7 11 12 14 16 17 19 20 21 22 23 24 25 26 28 30
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.08 1.44
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     348.3497 -0.06997782 0.9728653      NA   1     55 3551.86
##   2:  NA    2     345.4258 -0.06916086 0.9713413      NA   2     56 3553.03
##   3:  NA    3     341.9077 -0.06817865 0.9698283      NA   3     57 3554.14
##   4:  NA    4     337.2930 -0.06689179 0.9692806      NA   4     58 3555.24
##   5:  NA    5     332.1048 -0.06544582 0.9694987      NA   5     59 3556.39
##  ---                                                                       
## 209:  NA  209     136.9087 -0.01224002 0.9287390      NA 111    165 3675.94
## 210:  NA  210     136.1952 -0.01204309 0.9272251      NA 107    161 3671.42
## 211:  NA  211     135.9820 -0.01198924 0.9329675      NA 110    164 3674.76
## 212:  NA  212     135.4332 -0.01183877 0.9372692      NA 108    162 3672.57
## 213:  NA  213     135.2900 -0.01180141 0.9389441      NA 109    163 3673.66
##      endtime    oxy endoxy        rate
##   1: 3611.86 99.935 95.927 -0.06997782
##   2: 3613.03 99.897 95.838 -0.06916086
##   3: 3614.14 99.873 95.805 -0.06817865
##   4: 3615.24 99.925 95.762 -0.06689179
##   5: 3616.39 99.893 95.726 -0.06544582
##  ---                                  
## 209: 3735.94 91.976 91.049 -0.01224002
## 210: 3731.42 92.298 91.218 -0.01204309
## 211: 3734.76 92.076 91.058 -0.01198924
## 212: 3732.57 92.238 91.152 -0.01183877
## 213: 3733.66 92.163 91.113 -0.01180141
## 
## Regressions : 213 | Results : 213 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 213 adjusted rate(s):
## Rate          : -0.06997782
## Adjustment    : 0.0001214543
## Adjusted Rate : -0.07009928 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.93 and 1...

## ----- Selection complete. 33 rate(s) removed, 180 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 179 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     348.3497 -0.06997782 0.9728653      NA   1     55 3551.86
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 3611.86 99.935 95.927 -0.06997782 0.0001214543   -0.07009928 -0.07009928
##    oxy.unit time.unit  volume    mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04593 0.00058   NA 36 30 1.013253 -0.7168839
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -1236.007          NA  mgO2/hr/kg   -1236.007
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
                      Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
100	3	CARL249	CARL360	Arlington reef	373	0.00058	ch2	Dell	0.04593	2023-05-13	2024-06-14	good/good	36	30	428.9521	0.2487922	0.9878	1236.007	0.7168839	0.9728653	807.0547	0.4680917

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 102 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

4

Enter specimen data

Replicate = 4 
mass = 0.0004503
chamber = "ch1" 
Swim = "good/good"
chamber_vol = chamber1_dell
system1 = "Dell"
Notes=""

##--- time of trail ---## 
experiment_mmr_date <- "13 May 2023 12 30PM/Oxygen"
experiment_mmr_date2 <- "13 May 2023 12 30PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] -5.852136e-05

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 

## [1] -0.002269307

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.07 10.44
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME) 
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  6  8  9 10 11 12 13 14 15 16 18 19 20 21 22 23 26
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.07 10.44
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE)  

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:  14    1     335.0586 -0.02018689 0.998      NA 6346   6579 11769.91
## 2:  16    1     400.6679 -0.02353068 0.991      NA 7333   7566 12849.99
## 3:  17    1     435.5064 -0.02516601 0.999      NA 7825   8058 13389.93
## 4:  19    1     439.9248 -0.02359002 0.991      NA 8812   9045 14469.47
## 5:  20    1     487.4384 -0.02587190 0.996      NA 9306   9539 15010.30
## 6:  21    1     428.0032 -0.02118516 0.974      NA 9799  10032 15549.70
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 12025.09 97.706 92.119 -0.02018689 -0.001764542   -0.01842235 -0.01842235
## 2: 13104.83 97.875 92.054 -0.02353068 -0.002002525   -0.02152815 -0.02152815
## 3: 13644.61 98.583 92.060 -0.02516601 -0.002121496   -0.02304451 -0.02304451
## 4: 14724.33 98.663 92.677 -0.02359002 -0.002359418   -0.02123060 -0.02123060
## 5: 15264.90 99.174 92.359 -0.02587190 -0.002478573   -0.02339333 -0.02339333
## 6: 15805.01 99.053 93.282 -0.02118516 -0.002597521   -0.01858764 -0.01858764
##    oxy.unit time.unit volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.0465 0.0004503   NA 36 30 1.013253 -0.1907378
## 2:     %Air       sec 0.0465 0.0004503   NA 36 30 1.013253 -0.2228941
## 3:     %Air       sec 0.0465 0.0004503   NA 36 30 1.013253 -0.2385939
## 4:     %Air       sec 0.0465 0.0004503   NA 36 30 1.013253 -0.2198134
## 5:     %Air       sec 0.0465 0.0004503   NA 36 30 1.013253 -0.2422054
## 6:     %Air       sec 0.0465 0.0004503   NA 36 30 1.013253 -0.1924492
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -423.5795          NA  mgO2/hr/kg   -423.5795
## 2:   -494.9902          NA  mgO2/hr/kg   -494.9902
## 3:   -529.8554          NA  mgO2/hr/kg   -529.8554
## 4:   -488.1487          NA  mgO2/hr/kg   -488.1487
## 5:   -537.8756          NA  mgO2/hr/kg   -537.8756
## 6:   -427.3799          NA  mgO2/hr/kg   -427.3799
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple")  

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
100	4	CARL249	CARL360	Arlington reef	373	0.0004503	ch1	Dell	0.0465	2023-05-13	2024-06-14	good/good	36	30	495.65	0.2231912	0.9902

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.07 10.44
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  6  8  9 10 11 12 13 14 15 16 18 19 20 21 22 23 26
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.08 1.48
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     432.4408 -0.06999253 0.9901759      NA  84    137 4799.85
##   2:  NA    2     432.4127 -0.06998535 0.9901487      NA  85    138 4800.94
##   3:  NA    3     432.0604 -0.06991107 0.9899415      NA  86    139 4802.03
##   4:  NA    4     432.0059 -0.06990404 0.9899182      NA  83    136 4798.51
##   5:  NA    5     431.8879 -0.06987419 0.9898539      NA  87    140 4803.36
##  ---                                                                       
## 205:  NA  205     207.1476 -0.02310715 0.9724506      NA  37     90 4746.96
## 206:  NA  206     206.2229 -0.02291787 0.9734921      NA  41     94 4751.44
## 207:  NA  207     205.6453 -0.02279368 0.9762790      NA  38     91 4748.14
## 208:  NA  208     205.2530 -0.02271380 0.9769990      NA  40     93 4750.31
## 209:  NA  209     204.7465 -0.02260664 0.9788831      NA  39     92 4749.23
##      endtime    oxy endoxy        rate
##   1: 4859.85 96.293 92.465 -0.06999253
##   2: 4860.94 96.237 92.393 -0.06998535
##   3: 4862.03 96.175 92.345 -0.06991107
##   4: 4858.51 96.307 92.534 -0.06990404
##   5: 4863.36 96.116 92.228 -0.06987419
##  ---                                  
## 205: 4806.96 97.668 96.053 -0.02310715
## 206: 4811.44 97.441 95.750 -0.02291787
## 207: 4808.14 97.641 96.010 -0.02279368
## 208: 4810.31 97.489 95.816 -0.02271380
## 209: 4809.23 97.579 95.944 -0.02260664
## 
## Regressions : 209 | Results : 209 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 209 adjusted rate(s):
## Rate          : -0.06999253
## Adjustment    : -5.852136e-05
## Adjusted Rate : -0.06993401 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.93 and 1...

## ----- Selection complete. 19 rate(s) removed, 190 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 189 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     432.4408 -0.06999253 0.9901759      NA  84    137 4799.85
##    endtime    oxy endoxy        rate    adjustment rate.adjusted  rate.input
## 1: 4859.85 96.293 92.465 -0.06999253 -5.852136e-05   -0.06993401 -0.06993401
##    oxy.unit time.unit volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.0465 0.0004503   NA 36 30 1.013253 -0.7240695
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -1607.971          NA  mgO2/hr/kg   -1607.971
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
                      Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
100	4	CARL249	CARL360	Arlington reef	373	0.0004503	ch1	Dell	0.0465	2023-05-13	2024-06-14	good/good	36	30	495.65	0.2231912	0.9902	1607.971	0.7240695	0.9901759	1112.321	0.5008783

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 103 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

5

Enter specimen data

Replicate = 5 
mass = 0.0007720 
chamber = "ch4" 
Swim = "good/good"
chamber_vol = chamber4_asus
system1 = "Asus"
Notes=""

##--- time of trail ---## 
experiment_mmr_date_asus <- "13 May 2023 12 44PM/Oxygen"
experiment_mmr_date2_asus <- "13 May 2023 12 44PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] 0.001076512

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 

## [1] -0.0008635787

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.32 13.07
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 11 12 13 15 16 17 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.32 13.07
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.

## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve). 
## Ensure this is correct. The 'linear' adjustment has been performed regardless.

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 1 rate(s) removed, 20 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 14 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:   7    1     382.6597 -0.02601808 0.980      NA 2429   2618 10928.37
## 2:  11    1     485.5951 -0.02957571 0.985      NA 4020   4209 13089.28
## 3:  12    1     482.6028 -0.02818016 0.994      NA 4419   4608 13628.68
## 4:  17    1     552.0899 -0.02775387 0.995      NA 6411   6600 16328.50
## 5:  18    1     640.2116 -0.03207079 0.984      NA 6810   6999 16869.11
## 6:  19    1     611.8451 -0.02951783 0.994      NA 7209   7398 17409.36
##     endtime    oxy endoxy        rate    adjustment rate.adjusted  rate.input
## 1: 11183.62 98.245 91.654 -0.02601808 -0.0002037005   -0.02581438 -0.02581438
## 2: 13344.85 98.469 91.510 -0.02957571 -0.0007153763   -0.02886033 -0.02886033
## 3: 13884.35 98.513 90.932 -0.02818016 -0.0008431017   -0.02733706 -0.02733706
## 4: 16584.52 98.875 91.372 -0.02775387 -0.0014823785   -0.02627149 -0.02627149
## 5: 17124.92 99.157 90.644 -0.03207079 -0.0016103537   -0.03046044 -0.03046044
## 6: 17665.09 98.091 90.310 -0.02951783 -0.0017382590   -0.02777957 -0.02777957
##    oxy.unit time.unit  volume     mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04791 0.000772   NA 36 30 1.013253 -0.2753763
## 2:     %Air       sec 0.04791 0.000772   NA 36 30 1.013253 -0.3078693
## 3:     %Air       sec 0.04791 0.000772   NA 36 30 1.013253 -0.2916196
## 4:     %Air       sec 0.04791 0.000772   NA 36 30 1.013253 -0.2802527
## 5:     %Air       sec 0.04791 0.000772   NA 36 30 1.013253 -0.3249385
## 6:     %Air       sec 0.04791 0.000772   NA 36 30 1.013253 -0.2963402
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -356.7051          NA  mgO2/hr/kg   -356.7051
## 2:   -398.7944          NA  mgO2/hr/kg   -398.7944
## 3:   -377.7457          NA  mgO2/hr/kg   -377.7457
## 4:   -363.0216          NA  mgO2/hr/kg   -363.0216
## 5:   -420.9048          NA  mgO2/hr/kg   -420.9048
## 6:   -383.8603          NA  mgO2/hr/kg   -383.8603
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
100	5	CARL249	CARL360	Arlington reef	373	0.000772	ch4	Asus	0.04791	2023-05-13	2024-06-14	good/good	36	30	388.8654	0.3002041	0.9904

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 4.11
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  2  3  4  5  6  7  8  9 10 12 13 14 15 16 17 18 19 20 21 22
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 1.41
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0     slope_b1       rsq density row endrow    time
##   1:  NA    1     583.8845 -0.082489692 0.9960442      NA  12     57 5915.58
##   2:  NA    2     583.7551 -0.082469019 0.9960146      NA  11     56 5914.24
##   3:  NA    3     581.9935 -0.082173449 0.9956812      NA  10     55 5912.90
##   4:  NA    4     581.8203 -0.082141060 0.9953315      NA  13     58 5916.91
##   5:  NA    5     578.8979 -0.081653326 0.9950795      NA   9     54 5911.53
##  ---                                                                        
## 174:  NA  174     149.4734 -0.009755240 0.5563961      NA  52     97 5969.60
## 175:  NA  175     149.0420 -0.009672054 0.5707880      NA  48     93 5964.18
## 176:  NA  176     145.7005 -0.009123153 0.5890778      NA  51     96 5968.27
## 177:  NA  177     144.9184 -0.008986866 0.5982235      NA  49     94 5965.55
## 178:  NA  178     143.7919 -0.008801890 0.6105300      NA  50     95 5966.90
##      endtime    oxy endoxy         rate
##   1: 5975.58 95.822 91.100 -0.082489692
##   2: 5974.24 95.899 91.107 -0.082469019
##   3: 5972.90 95.940 91.174 -0.082173449
##   4: 5976.91 95.746 91.141 -0.082141060
##   5: 5971.53 96.022 91.316 -0.081653326
##  ---                                   
## 174: 6029.60 91.532 90.099 -0.009755240
## 175: 6024.18 91.922 90.717 -0.009672054
## 176: 6028.27 91.603 90.232 -0.009123153
## 177: 6025.55 91.844 90.566 -0.008986866
## 178: 6026.90 91.722 90.403 -0.008801890
## 
## Regressions : 178 | Results : 178 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 178 adjusted rate(s):
## Rate          : -0.08248969
## Adjustment    : 0.001076512
## Adjusted Rate : -0.0835662 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.93 and 1...

## ----- Selection complete. 54 rate(s) removed, 124 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 123 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     583.8845 -0.08248969 0.9960442      NA  12     57 5915.58
##    endtime    oxy endoxy        rate  adjustment rate.adjusted rate.input
## 1: 5975.58 95.822   91.1 -0.08248969 0.001076512    -0.0835662 -0.0835662
##    oxy.unit time.unit  volume     mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04791 0.000772   NA 36 30 1.013253 -0.8914473
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -1154.725          NA  mgO2/hr/kg   -1154.725
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
100	5	CARL249	CARL360	Arlington reef	373	0.000772	ch4	Asus	0.04791	2023-05-13	2024-06-14	good/good	36	30	388.8654	0.3002041	0.9904	1154.725	0.8914473	0.9960442	765.8591	0.5912433
### Expor	ting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 104 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

6

Enter specimen data

Replicate = 6 
mass = 0.0005870 
chamber = "ch3" 
Swim = "good/good"
chamber_vol = chamber3_asus
system1 = "Asus"
Notes=""

##--- time of trail ---## 
experiment_mmr_date_asus <- "13 May 2023 12 53PM/Oxygen"
experiment_mmr_date2_asus <- "13 May 2023 12 53PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] 0.0002177651

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 

## [1] -0.002677291

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.32 13.07
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 11 12 13 15 16 17 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.32 13.07
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=245, 
                              by="time", 
                              plot=TRUE) 

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.

## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve). 
## Ensure this is correct. The 'linear' adjustment has been performed regardless.

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:   8    1     401.8909 -0.02662167 0.993      NA 2829   3011 11468.76
## 2:   9    1     416.4061 -0.02662055 0.990      NA 3229   3411 12009.08
## 3:  11    1     436.3074 -0.02587229 0.979      NA 4020   4202 13089.28
## 4:  13    1     441.8821 -0.02428353 0.978      NA 4816   4997 14169.03
## 5:  18    1     396.2199 -0.01769467 0.974      NA 6810   6991 16869.11
## 6:  21    1     437.7328 -0.01836936 0.986      NA 8007   8188 18489.32
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 11714.72 96.685 90.392 -0.02662167 -0.001881889   -0.02473978 -0.02473978
## 2: 12255.06 97.039 90.697 -0.02662055 -0.002072795   -0.02454775 -0.02454775
## 3: 13335.40 98.054 91.241 -0.02587229 -0.002454470   -0.02341782 -0.02341782
## 4: 14414.02 97.756 92.139 -0.02428353 -0.002835761   -0.02144777 -0.02144777
## 5: 17114.09 98.003 93.702 -0.01769467 -0.003789736   -0.01390493 -0.01390493
## 6: 18734.12 97.999 93.293 -0.01836936 -0.004362147   -0.01400722 -0.01400722
##    oxy.unit time.unit  volume     mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04551 0.000587   NA 36 30 1.013253 -0.2506926
## 2:     %Air       sec 0.04551 0.000587   NA 36 30 1.013253 -0.2487467
## 3:     %Air       sec 0.04551 0.000587   NA 36 30 1.013253 -0.2372969
## 4:     %Air       sec 0.04551 0.000587   NA 36 30 1.013253 -0.2173341
## 5:     %Air       sec 0.04551 0.000587   NA 36 30 1.013253 -0.1409011
## 6:     %Air       sec 0.04551 0.000587   NA 36 30 1.013253 -0.1419376
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -427.0743          NA  mgO2/hr/kg   -427.0743
## 2:   -423.7593          NA  mgO2/hr/kg   -423.7593
## 3:   -404.2537          NA  mgO2/hr/kg   -404.2537
## 4:   -370.2454          NA  mgO2/hr/kg   -370.2454
## 5:   -240.0360          NA  mgO2/hr/kg   -240.0360
## 6:   -241.8018          NA  mgO2/hr/kg   -241.8018
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
100	6	CARL249	CARL360	Arlington reef	373	0.000587	ch3	Asus	0.04551	2023-05-13	2024-06-14	good/good	36	30	373.4269	0.2192016	0.9852

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 4.11
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 12 13 14 15 17 18 19 21 22 23 24
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 1.42
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     573.3481 -0.07301918 0.9856640      NA 144    189 6618.11
##   2:  NA    2     573.1975 -0.07299836 0.9855884      NA 143    188 6616.76
##   3:  NA    3     571.8223 -0.07279344 0.9847715      NA 142    187 6615.41
##   4:  NA    4     571.5421 -0.07274603 0.9847927      NA 145    190 6619.46
##   5:  NA    5     569.2432 -0.07240735 0.9831847      NA 141    186 6614.06
##  ---                                                                       
## 173:  NA  173     337.4840 -0.03737809 0.9868192      NA  89    134 6543.83
## 174:  NA  174     335.9278 -0.03714044 0.9881461      NA  88    133 6542.50
## 175:  NA  175     335.7616 -0.03711301 0.9881633      NA  85    130 6538.42
## 176:  NA  176     334.7633 -0.03696254 0.9889250      NA  87    132 6541.14
## 177:  NA  177     334.5951 -0.03693626 0.9890376      NA  86    131 6539.77
##      endtime    oxy endoxy        rate
##   1: 6678.11 89.866 85.982 -0.07301918
##   2: 6676.76 89.882 86.052 -0.07299836
##   3: 6675.41 89.911 86.111 -0.07279344
##   4: 6679.46 89.887 85.922 -0.07274603
##   5: 6674.06 89.963 86.204 -0.07240735
##  ---                                  
## 173: 6603.83 92.999 90.463 -0.03737809
## 174: 6602.50 93.033 90.566 -0.03714044
## 175: 6598.42 93.351 90.804 -0.03711301
## 176: 6601.14 93.114 90.664 -0.03696254
## 177: 6599.77 93.237 90.745 -0.03693626
## 
## Regressions : 177 | Results : 177 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 177 adjusted rate(s):
## Rate          : -0.07301918
## Adjustment    : 0.0002177651
## Adjusted Rate : -0.07323695 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.93 and 1...

## ----- Selection complete. 0 rate(s) removed, 177 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 176 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1      rsq density row endrow    time
## 1:  NA    1     573.3481 -0.07301918 0.985664      NA 144    189 6618.11
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 6678.11 89.866 85.982 -0.07301918 0.0002177651   -0.07323695 -0.07323695
##    oxy.unit time.unit  volume     mass area  S  t        P  rate.abs
## 1:     %Air       sec 0.04551 0.000587   NA 36 30 1.013253 -0.742123
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -1264.264          NA  mgO2/hr/kg   -1264.264
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
100	6	CARL249	CARL360	Arlington reef	373	0.000587	ch3	Asus	0.04551	2023-05-13	2024-06-14	good/good	36	30	373.4269	0.2192016	0.9852	1264.264	0.742123	0.985664	890.8372	0.5229214
### Expor	ting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 105 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

7

Enter specimen data

Replicate = 7 
mass = 0.0006606 
chamber = "ch2" 
Swim = "good/good"
chamber_vol = chamber2_asus
system1 = "Asus"
Notes=""

##--- time of trail ---## 
experiment_mmr_date_asus <- "13 May 2023 01 03PM/Oxygen"
experiment_mmr_date2_asus <- "13 May 2023 01 03PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] 0.0008508406

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 

## [1] -0.00206665

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.32 13.07
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 11 12 13 15 16 17 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.32 13.07
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=245, 
                              by="time", 
                              plot=TRUE) 

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.

## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve). 
## Ensure this is correct. The 'linear' adjustment has been performed regardless.

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 1 rate(s) removed, 20 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 14 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:   6    1     324.7317 -0.02190726 0.993      NA 2030   2211 10389.22
## 2:   9    1     368.6146 -0.02253713 0.992      NA 3229   3411 12009.08
## 3:  10    1     375.9054 -0.02214872 0.995      NA 3629   3811 12549.34
## 4:  11    1     405.3318 -0.02345801 0.997      NA 4020   4202 13089.28
## 5:  14    1     423.6132 -0.02212574 0.994      NA 5215   5396 14709.16
## 6:  20    1     513.4955 -0.02313632 0.992      NA 7608   7789 17949.07
##     endtime    oxy endoxy        rate    adjustment rate.adjusted  rate.input
## 1: 10633.82 97.223 91.920 -0.02190726 -0.0008804697   -0.02102679 -0.02102679
## 2: 12255.06 97.882 92.163 -0.02253713 -0.0014574699   -0.02107966 -0.02107966
## 3: 12795.28 97.858 92.175 -0.02214872 -0.0016498235   -0.02049890 -0.02049890
## 4: 13335.40 98.157 92.276 -0.02345801 -0.0018421023   -0.02161591 -0.02161591
## 5: 14954.27 98.251 92.485 -0.02212574 -0.0024186841   -0.01970706 -0.01970706
## 6: 18194.02 98.279 92.660 -0.02313632 -0.0035722323   -0.01956409 -0.01956409
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04573 0.0006606   NA 36 30 1.013253 -0.2140982
## 2:     %Air       sec 0.04573 0.0006606   NA 36 30 1.013253 -0.2146365
## 3:     %Air       sec 0.04573 0.0006606   NA 36 30 1.013253 -0.2087231
## 4:     %Air       sec 0.04573 0.0006606   NA 36 30 1.013253 -0.2200967
## 5:     %Air       sec 0.04573 0.0006606   NA 36 30 1.013253 -0.2006605
## 6:     %Air       sec 0.04573 0.0006606   NA 36 30 1.013253 -0.1992047
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -324.0966          NA  mgO2/hr/kg   -324.0966
## 2:   -324.9114          NA  mgO2/hr/kg   -324.9114
## 3:   -315.9599          NA  mgO2/hr/kg   -315.9599
## 4:   -333.1769          NA  mgO2/hr/kg   -333.1769
## 5:   -303.7549          NA  mgO2/hr/kg   -303.7549
## 6:   -301.5512          NA  mgO2/hr/kg   -301.5512
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
100	7	CARL249	CARL360	Arlington reef	373	0.0006606	ch2	Asus	0.04573	2023-05-13	2024-06-14	good/good	36	30	320.38	0.211643	0.9942

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 4.11
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = 5259, # custom  
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7 10 11 12 13 15 16 18 19 20 21 24 25 26
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 1.38
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##     rep rank intercept_b0    slope_b1       rsq density row endrow    time
##  1:  NA    1     704.8510 -0.08633115 0.9981164      NA  13     58 7032.88
##  2:  NA    2     704.2120 -0.08624074 0.9981195      NA  14     59 7034.24
##  3:  NA    3     703.6810 -0.08616544 0.9981203      NA  12     57 7031.53
##  4:  NA    4     703.0230 -0.08607205 0.9981423      NA  11     56 7030.19
##  5:  NA    5     702.1531 -0.08594941 0.9981398      NA  15     60 7035.60
## ---                                                                       
## 27:  NA   27     685.5334 -0.08359882 0.9979191      NA  27     72 7051.79
## 28:  NA   28     684.1802 -0.08340760 0.9978620      NA  28     73 7053.15
## 29:  NA   29     682.4174 -0.08315842 0.9977262      NA  29     74 7054.51
## 30:  NA   30     679.6539 -0.08276766 0.9972590      NA  30     75 7055.87
## 31:  NA   31     676.0106 -0.08225244 0.9963343      NA  31     76 7057.20
##     endtime    oxy endoxy        rate
##  1: 7092.88 97.724 92.472 -0.08633115
##  2: 7094.24 97.668 92.410 -0.08624074
##  3: 7091.53 97.879 92.586 -0.08616544
##  4: 7090.19 97.982 92.680 -0.08607205
##  5: 7095.60 97.594 92.340 -0.08594941
## ---                                  
## 27: 7111.79 96.129 91.016 -0.08359882
## 28: 7113.15 95.975 90.956 -0.08340760
## 29: 7114.51 95.844 90.890 -0.08315842
## 30: 7115.87 95.689 90.890 -0.08276766
## 31: 7117.20 95.541 90.888 -0.08225244
## 
## Regressions : 31 | Results : 31 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 31 adjusted rate(s):
## Rate          : -0.08633115
## Adjustment    : 0.0008508406
## Adjusted Rate : -0.08718199 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.93 and 1...

## ----- Selection complete. 0 rate(s) removed, 31 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 30 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1      704.851 -0.08633115 0.9981164      NA  13     58 7032.88
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 7092.88 97.724 92.472 -0.08633115 0.0008508406   -0.08718199 -0.08718199
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04573 0.0006606   NA 36 30 1.013253 -0.8877012
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:    -1343.78          NA  mgO2/hr/kg    -1343.78
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
100	7	CARL249	CARL360	Arlington reef	373	0.0006606	ch2	Asus	0.04573	2023-05-13	2024-06-14	good/good	36	30	320.38	0.211643	0.9942	1343.78	0.8877012	0.9981164	1023.4	0.6760582
### Expor	ting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 106 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

8

Enter specimen data

Replicate = 8 
mass = 0.0005530 
chamber = "ch1" 
Swim = "good/good"
chamber_vol = chamber1_asus
system1 = "Asus"
Notes=""

##--- time of trail ---## 
experiment_mmr_date_asus <- "13 May 2023 01 13PM/Oxygen"
experiment_mmr_date2_asus <- "13 May 2023 01 13PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] -0.001065898

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 

## [1] -0.0002845502

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.32 13.07
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 11 12 13 15 16 17 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.32 13.07
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=245, 
                              by="time", 
                              plot=TRUE) 

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:  12    1     293.9377 -0.01430860 0.977      NA 4419   4600 13628.68
## 2:  13    1     298.5665 -0.01407149 0.983      NA 4816   4997 14169.03
## 3:  15    1     301.6732 -0.01327192 0.969      NA 5614   5795 15249.57
## 4:  16    1     306.9796 -0.01315136 0.970      NA 6012   6193 15788.58
## 5:  17    1     329.1421 -0.01406912 0.971      NA 6411   6592 16328.50
## 6:  20    1     334.3882 -0.01312230 0.980      NA 7608   7789 17949.07
##     endtime    oxy endoxy        rate    adjustment rate.adjusted  rate.input
## 1: 13873.54 98.673 95.210 -0.01430860 -2.933125e-04   -0.01401529 -0.01401529
## 2: 14414.02 98.872 95.499 -0.01407149 -2.417806e-04   -0.01382971 -0.01382971
## 3: 15494.87 98.889 95.631 -0.01327192 -1.387298e-04   -0.01313319 -0.01313319
## 4: 16033.31 98.917 95.882 -0.01315136 -8.735914e-05   -0.01306400 -0.01306400
## 5: 16573.66 98.928 95.608 -0.01406912 -3.585400e-05   -0.01403327 -0.01403327
## 6: 18194.02 98.519 95.353 -0.01312230  1.186672e-04   -0.01324096 -0.01324096
##    oxy.unit time.unit  volume     mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04565 0.000553   NA 36 30 1.013253 -0.1424563
## 2:     %Air       sec 0.04565 0.000553   NA 36 30 1.013253 -0.1405700
## 3:     %Air       sec 0.04565 0.000553   NA 36 30 1.013253 -0.1334903
## 4:     %Air       sec 0.04565 0.000553   NA 36 30 1.013253 -0.1327871
## 5:     %Air       sec 0.04565 0.000553   NA 36 30 1.013253 -0.1426390
## 6:     %Air       sec 0.04565 0.000553   NA 36 30 1.013253 -0.1345858
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -257.6064          NA  mgO2/hr/kg   -257.6064
## 2:   -254.1954          NA  mgO2/hr/kg   -254.1954
## 3:   -241.3930          NA  mgO2/hr/kg   -241.3930
## 4:   -240.1213          NA  mgO2/hr/kg   -240.1213
## 5:   -257.9368          NA  mgO2/hr/kg   -257.9368
## 6:   -243.3740          NA  mgO2/hr/kg   -243.3740
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
100	8	CARL249	CARL360	Arlington reef	373	0.000553	ch1	Asus	0.04565	2023-05-13	2024-06-14	good/good	36	30	250.9011	0.1387483	0.976

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.32 13.07
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  3  4  5  6  7  8  9 10 11 13 14 15 16 17 18 19 20 21 23
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 1.44
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     522.1141 -0.05511474 0.9969994      NA 114    159 7797.94
##   2:  NA    2     521.9780 -0.05509777 0.9969680      NA 113    158 7796.57
##   3:  NA    3     521.9730 -0.05509616 0.9969620      NA 115    160 7799.29
##   4:  NA    4     521.5660 -0.05504551 0.9968818      NA 112    157 7795.21
##   5:  NA    5     521.3839 -0.05502055 0.9968495      NA 116    161 7800.64
##  ---                                                                       
## 173:  NA  173     302.8310 -0.02727485 0.9776066      NA 173    218 7877.64
## 174:  NA  174     296.7104 -0.02650130 0.9809523      NA 174    219 7879.01
## 175:  NA  175     291.7542 -0.02587502 0.9831980      NA 175    220 7880.35
## 176:  NA  176     288.6302 -0.02548021 0.9831713      NA 176    221 7881.69
## 177:  NA  177     286.4035 -0.02519895 0.9834991      NA 177    222 7883.03
##      endtime    oxy endoxy        rate
##   1: 7857.94 92.307 89.093 -0.05511474
##   2: 7856.57 92.373 89.155 -0.05509777
##   3: 7859.29 92.219 89.056 -0.05509616
##   4: 7855.21 92.466 89.187 -0.05504551
##   5: 7860.64 92.194 88.967 -0.05502055
##  ---                                  
## 173: 7937.64 88.270 86.424 -0.02727485
## 174: 7939.01 88.186 86.416 -0.02650130
## 175: 7940.35 88.079 86.379 -0.02587502
## 176: 7941.69 87.934 86.346 -0.02548021
## 177: 7943.03 87.875 86.283 -0.02519895
## 
## Regressions : 177 | Results : 177 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 177 adjusted rate(s):
## Rate          : -0.05511474
## Adjustment    : -0.001065898
## Adjusted Rate : -0.05404884 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.93 and 1...

## ----- Selection complete. 0 rate(s) removed, 177 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 176 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     522.1141 -0.05511474 0.9969994      NA 114    159 7797.94
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 7857.94 92.307 89.093 -0.05511474 -0.001065898   -0.05404884 -0.05404884
##    oxy.unit time.unit  volume     mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04565 0.000553   NA 36 30 1.013253 -0.5493713
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -993.4382          NA  mgO2/hr/kg   -993.4382
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
100	8	CARL249	CARL360	Arlington reef	373	0.000553	ch1	Asus	0.04565	2023-05-13	2024-06-14	good/good	36	30	250.9011	0.1387483	0.976	993.4382	0.5493713	0.9969994	742.5371	0.410623
### Expor	ting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 107 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)